Sidharth Gopakumar
Product Manager, AI | Molecule Software
Boston, Massachusetts, United States
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Sidharth Gopakumar is a product manager at Molecule Software, where he leads product development for an energy trading platform used by hedge funds and Fortune 100 energy companies, processing nearly $100B in commodity value daily. Before that, he built conversational GenAI and vector search infrastructure at a YCombinator-backed enterprise startup, taking the platform from zero to 10 enterprise customers. He has published IEEE research on predictive AI frameworks and prescriptive analytics in operations.
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From Prototype to Production: A PM's Guide to Getting LLM Features Shipped
Most organizations don't struggle to build an AI prototype. They struggle to get it into production. The blockers aren't technical — they're organizational: legal wants auditability, compliance wants guardrails, expert users will catch every mistake, and leadership wants to know when the model is wrong.
This session is a product manager's guide to the decisions that determine whether an LLM feature ships or stalls. Drawing on experience building GenAI platforms at a YCombinator-backed startup and leading AI product development at an enterprise SaaS used by hedge funds and Fortune 100 companies, the talk covers: how to scope what the model can and can't own in a high-stakes workflow; how to design evaluation frameworks that satisfy both product and compliance teams; how to communicate model limitations to expert users who will test your system aggressively; and how to build the organizational trust that gets AI features past legal, risk, and the CISO.
Practical and non-commercial, designed for product leaders, AI practitioners, and anyone trying to close the gap between a working demo and a trusted production system.
AI Governance for High-Stakes Platforms: Beyond Responsible AI Checklists
Most AI governance frameworks are written for products where a bad output is embarrassing. In financial services and commodity trading, a bad output can trigger regulatory exposure, erode client trust, or generate incorrect positions.
This talk moves past the checklist approach to examine what AI governance actually looks like when deployed in high-stakes production environments. Drawing on experience deploying AI in an energy trading platform processing nearly $100B in commodity value daily, the session covers: auditability requirements for AI decisions that affect financial positions; model drift detection strategies that catch degradation before users do; rollback protocols that don't require taking down the entire system; and how to design human-in-the-loop checkpoints that preserve oversight without killing the user experience.
The talk is practitioner-led and non-commercial, designed for risk leaders, product security teams, and technology executives who are past the policy stage and trying to operationalize governance in production AI systems.
Key takeaways: a practical architecture for AI auditability in high-value transaction environments; how to detect and respond to model drift without disrupting operations; design patterns for human oversight that scale; and where most enterprise AI governance frameworks break down in practice.
Sidharth Gopakumar
Product Manager, AI | Molecule Software
Boston, Massachusetts, United States
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